In [1]:
import pandas as pd
import plotly.express as px
import plotly.graph_objects as go
from plotly.subplots import make_subplots
In [2]:
races_data = pd.read_csv('races.csv')
diverstanding_data = pd.read_csv('driver_standings.csv')
drivers_data = pd.read_csv('drivers.csv')
constructorstanding_data = pd.read_csv('constructor_standings.csv')
constructors_data = pd.read_csv('constructors.csv')
constructor_race_data = pd.read_csv('constructor_results.csv')
In [3]:
data_2011 = races_data.where(races_data['year']==2011).dropna()
data_2011 = data_2011.merge(diverstanding_data,on='raceId').dropna()
data_2011 = data_2011.merge(drivers_data,on='driverId').dropna()
data_2011.drop(columns=['date','time','url_x','url_y','driverStandingsId','driverId','dob','number','nationality','raceId'],inplace=True)
data_2011 = data_2011.sort_values(['round','position'])
data_2011
Out[3]:
year round circuitId name points position positionText wins driverRef code forename surname
361 2011.0 1.0 1.0 Australian Grand Prix 25.0 1 1 1 vettel VET Sebastian Vettel
342 2011.0 1.0 1.0 Australian Grand Prix 18.0 2 2 0 hamilton HAM Lewis Hamilton
323 2011.0 1.0 1.0 Australian Grand Prix 15.0 3 3 0 petrov PET Vitaly Petrov
304 2011.0 1.0 1.0 Australian Grand Prix 12.0 4 4 0 alonso ALO Fernando Alonso
285 2011.0 1.0 1.0 Australian Grand Prix 10.0 5 5 0 webber WEB Mark Webber
... ... ... ... ... ... ... ... ... ... ... ... ...
132 2011.0 19.0 18.0 Brazilian Grand Prix 0.0 24 24 0 ambrosio DAM Jérôme d'Ambrosio
113 2011.0 19.0 18.0 Brazilian Grand Prix 0.0 25 25 0 glock GLO Timo Glock
451 2011.0 19.0 18.0 Brazilian Grand Prix 0.0 26 26 0 karthikeyan KAR Narain Karthikeyan
475 2011.0 19.0 18.0 Brazilian Grand Prix 0.0 27 27 0 ricciardo RIC Daniel Ricciardo
485 2011.0 19.0 18.0 Brazilian Grand Prix 0.0 28 28 0 chandhok CHA Karun Chandhok

494 rows × 12 columns

In [4]:
data_teams_2011 = races_data.where(races_data['year']==2011).dropna()
data_teams_2011 = data_teams_2011.merge(constructorstanding_data,on='raceId').dropna()
data_teams_2011 = data_teams_2011.merge(constructors_data,on='constructorId').dropna()
data_teams_2011.drop(columns=['date','time','url_x','url_y','constructorStandingsId','constructorId','nationality','raceId'],inplace=True)
data_teams_2011 = data_teams_2011.sort_values(['round','position'])
data_teams_2011.head(30)
Out[4]:
year round circuitId name_x points position positionText wins constructorRef name_y
171 2011.0 1.0 1.0 Australian Grand Prix 35.0 1 1 1 red_bull Red Bull
152 2011.0 1.0 1.0 Australian Grand Prix 26.0 2 2 0 mclaren McLaren
114 2011.0 1.0 1.0 Australian Grand Prix 18.0 3 3 0 ferrari Ferrari
133 2011.0 1.0 1.0 Australian Grand Prix 15.0 4 4 0 renault Renault
95 2011.0 1.0 1.0 Australian Grand Prix 4.0 5 5 0 toro_rosso Toro Rosso
76 2011.0 1.0 1.0 Australian Grand Prix 3.0 6 6 0 force_india Force India
57 2011.0 1.0 1.0 Australian Grand Prix 0.0 7 7 0 lotus_racing Lotus
38 2011.0 1.0 1.0 Australian Grand Prix 0.0 8 8 0 virgin Virgin
19 2011.0 1.0 1.0 Australian Grand Prix 0.0 9 9 0 williams Williams
0 2011.0 1.0 1.0 Australian Grand Prix 0.0 10 10 0 mercedes Mercedes
172 2011.0 2.0 2.0 Malaysian Grand Prix 72.0 1 1 2 red_bull Red Bull
153 2011.0 2.0 2.0 Malaysian Grand Prix 48.0 2 2 0 mclaren McLaren
115 2011.0 2.0 2.0 Malaysian Grand Prix 36.0 3 3 0 ferrari Ferrari
134 2011.0 2.0 2.0 Malaysian Grand Prix 30.0 4 4 0 renault Renault
190 2011.0 2.0 2.0 Malaysian Grand Prix 6.0 5 5 0 sauber Sauber
96 2011.0 2.0 2.0 Malaysian Grand Prix 4.0 6 6 0 toro_rosso Toro Rosso
77 2011.0 2.0 2.0 Malaysian Grand Prix 4.0 7 7 0 force_india Force India
1 2011.0 2.0 2.0 Malaysian Grand Prix 2.0 8 8 0 mercedes Mercedes
58 2011.0 2.0 2.0 Malaysian Grand Prix 0.0 9 9 0 lotus_racing Lotus
39 2011.0 2.0 2.0 Malaysian Grand Prix 0.0 10 10 0 virgin Virgin
20 2011.0 2.0 2.0 Malaysian Grand Prix 0.0 11 11 0 williams Williams
208 2011.0 2.0 2.0 Malaysian Grand Prix 0.0 12 12 0 hrt HRT
173 2011.0 3.0 17.0 Chinese Grand Prix 105.0 1 1 2 red_bull Red Bull
154 2011.0 3.0 17.0 Chinese Grand Prix 85.0 2 2 1 mclaren McLaren
116 2011.0 3.0 17.0 Chinese Grand Prix 50.0 3 3 0 ferrari Ferrari
135 2011.0 3.0 17.0 Chinese Grand Prix 32.0 4 4 0 renault Renault
2 2011.0 3.0 17.0 Chinese Grand Prix 16.0 5 5 0 mercedes Mercedes
191 2011.0 3.0 17.0 Chinese Grand Prix 7.0 6 6 0 sauber Sauber
97 2011.0 3.0 17.0 Chinese Grand Prix 4.0 7 7 0 toro_rosso Toro Rosso
78 2011.0 3.0 17.0 Chinese Grand Prix 4.0 8 8 0 force_india Force India
In [5]:
#teams_color_2011 = {'Red Bull Racing':dict(color='darkblue'),
#                   'McLaren':dict(color='orange'),
#                    'Ferrari':dict(color='red'),
#                    'Mercedes':dict(color='teal'),
#                    'Renault':dict(color='yellow'),
#                    'Williams':dict(color='')
#                   }
In [6]:
#drivers = pd.DataFrame()
constructors = data_teams_2011['name_y'].unique()
drivers = data_2011['code'].unique()
#drivers['color'] = []
In [7]:
fig1 = go.Figure()
fig2 = go.Figure()

for i in drivers:
    d = data_2011.where(data_2011['code'] == i).dropna()
    fig1.add_trace(go.Scatter(x=d['name'],y=d['points'],name=i,mode='lines+markers'))
    fig2.add_trace(go.Scatter(x=d['name'],y=d['position'],name=i,mode='lines+markers',hovertext=d.points))
fig1.show()
In [8]:
fig2['layout']['yaxis']['autorange'] = "reversed"
fig2.show()
In [9]:
fig3 = go.Figure()
fig4 = go.Figure()

for i in constructors:
    d = data_teams_2011.where(data_teams_2011['name_y'] == i).dropna()
    fig3.add_trace(go.Scatter(x=d['name_x'],y=d['points'],name=i,mode='lines+markers'))
    fig4.add_trace(go.Scatter(x=d['name_x'],y=d['position'],name=i,mode='lines+markers',hovertext=d.points))
fig3.show()
In [10]:
fig4['layout']['yaxis']['autorange'] = "reversed"
fig4.show()
In [11]:
data_2012 = races_data.where(races_data['year']==2012).dropna()
data_2012 = data_2012.merge(diverstanding_data,on='raceId').dropna()
data_2012 = data_2012.merge(drivers_data,on='driverId').dropna()
data_2012.drop(columns=['date','time','url_x','url_y','driverStandingsId','driverId','dob','number','nationality','raceId'],inplace=True)
data_2012 = data_2012.sort_values(['round','position'])
data_2012.head(30)

drivers = data_2012['code'].unique()
In [12]:
fig3 = go.Figure()
fig4 = go.Figure()
for i in drivers:
    d = data_2012.where(data_2012['code'] == i).dropna().sort_values(by='round') 
    fig3.add_traces(go.Scatter(x=d['name'],y=d['points'],name=i,mode='lines+markers'))
    fig4.add_traces(go.Scatter(x=d['name'],y=d['position'],name=i,mode='lines+markers',hovertext=d.points))
fig3.show()
In [13]:
fig4['layout']['yaxis']['autorange'] = "reversed"
fig4.show()
In [14]:
data_2013 = races_data.where(races_data['year']==2013).dropna()
data_2013 = data_2013.merge(diverstanding_data,on='raceId').dropna()
data_2013 = data_2013.merge(drivers_data,on='driverId').dropna()
data_2013.drop(columns=['date','time','url_x','url_y','driverStandingsId','driverId','dob','number','nationality','raceId'],inplace=True)
data_2013 = data_2013.sort_values(['round','position'])
data_2013
drivers = data_2013['code'].unique()
In [15]:
fig5 = go.Figure()
fig6 = go.Figure()
for i in drivers:
    d = data_2013.where(data_2013['code'] == i).dropna().sort_values(by='round') 
    fig5.add_traces(go.Scatter(x=d['name'],y=d['points'],name=i,mode='lines+markers'))
    fig6.add_traces(go.Scatter(x=d['name'],y=d['position'],name=i,mode='lines+markers',hovertext=d.points))
fig5.show()
In [16]:
fig6['layout']['yaxis']['autorange'] = "reversed"
fig6.show()
In [17]:
data_2014 = races_data.where(races_data['year']==2014).dropna()
data_2014 = data_2014.merge(diverstanding_data,on='raceId').dropna()
data_2014 = data_2014.merge(drivers_data,on='driverId').dropna()
data_2014.drop(columns=['date','time','url_x','url_y','driverStandingsId','driverId','dob','number','nationality','raceId'],inplace=True)
data_2014 = data_2014.sort_values(['round','position'])
data_2014
drivers = data_2014['code'].unique()
In [18]:
fig7 = go.Figure()
fig8 = go.Figure()
for i in drivers:
    d = data_2014.where(data_2014['code'] == i).dropna().sort_values(by='round') 
    fig7.add_traces(go.Scatter(x=d['name'],y=d['points'],name=i,mode='lines+markers'))
    fig8.add_traces(go.Scatter(x=d['name'],y=d['position'],name=i,mode='lines+markers',hovertext=d.points))
fig7.show()
In [19]:
fig8['layout']['yaxis']['autorange'] = "reversed"
fig8.show()
In [20]:
data_2015 = races_data.where(races_data['year']==2015).dropna()
data_2015 = data_2015.merge(diverstanding_data,on='raceId').dropna()
data_2015 = data_2015.merge(drivers_data,on='driverId').dropna()
data_2015.drop(columns=['date','time','url_x','url_y','driverStandingsId','driverId','dob','number','nationality','raceId'],inplace=True)
data_2015 = data_2015.sort_values(['round','position'])
data_2015
drivers = data_2015['code'].unique()
In [21]:
fig9 = go.Figure()
fig10 = go.Figure()
for i in drivers:
    d = data_2015.where(data_2015['code'] == i).dropna().sort_values(by='round') 
    fig9.add_traces(go.Scatter(x=d['name'],y=d['points'],name=i,mode='lines+markers'))
    fig10.add_traces(go.Scatter(x=d['name'],y=d['position'],name=i,mode='lines+markers',hovertext=d.points))
fig9.show()
In [22]:
fig10['layout']['yaxis']['autorange'] = "reversed"
fig10.show()
In [23]:
data_2016 = races_data.where(races_data['year']==2016).dropna()
data_2016 = data_2016.merge(diverstanding_data,on='raceId').dropna()
data_2016 = data_2016.merge(drivers_data,on='driverId').dropna()
data_2016.drop(columns=['date','time','url_x','url_y','driverStandingsId','driverId','dob','number','nationality','raceId'],inplace=True)
data_2016 = data_2016.sort_values(['round','position'])
data_2016
drivers = data_2016['code'].unique()
In [24]:
fig11 = go.Figure()
fig12 = go.Figure()
for i in drivers:
    d = data_2016.where(data_2016['code'] == i).dropna().sort_values(by='round') 
    fig11.add_traces(go.Scatter(x=d['name'],y=d['points'],name=i,mode='lines+markers'))
    fig12.add_traces(go.Scatter(x=d['name'],y=d['position'],name=i,mode='lines+markers',hovertext=d.points))
fig11.show()
In [25]:
fig12['layout']['yaxis']['autorange'] = "reversed"
fig12.show()
In [26]:
data_2017 = races_data.where(races_data['year']==2017).dropna()
data_2017 = data_2017.merge(diverstanding_data,on='raceId').dropna()
data_2017 = data_2017.merge(drivers_data,on='driverId').dropna()
data_2017.drop(columns=['date','time','url_x','url_y','driverStandingsId','driverId','dob','number','nationality','raceId'],inplace=True)
data_2017 = data_2017.sort_values(['round','position'])
data_2017
drivers = data_2017['code'].unique()
In [27]:
fig13 = go.Figure()
fig14 = go.Figure()
for i in drivers:
    d = data_2017.where(data_2017['code'] == i).dropna().sort_values(by='round') 
    fig13.add_traces(go.Scatter(x=d['name'],y=d['points'],name=i,mode='lines+markers'))
    fig14.add_traces(go.Scatter(x=d['name'],y=d['position'],name=i,mode='lines+markers',hovertext=d.points))
fig13.show()
In [28]:
fig14['layout']['yaxis']['autorange'] = "reversed"
fig14.show()
In [29]:
data_2018 = races_data.where(races_data['year']==2018).dropna()
data_2018 = data_2018.merge(diverstanding_data,on='raceId').dropna()
data_2018 = data_2018.merge(drivers_data,on='driverId').dropna()
data_2018.drop(columns=['date','time','url_x','url_y','driverStandingsId','driverId','dob','number','nationality','raceId'],inplace=True)
data_2018 = data_2018.sort_values(['round','position'])
data_2018
drivers = data_2018['code'].unique()
In [30]:
fig15 = go.Figure()
fig16 = go.Figure()
for i in drivers:
    d = data_2018.where(data_2018['code'] == i).dropna().sort_values(by='round') 
    fig15.add_traces(go.Scatter(x=d['name'],y=d['points'],name=i,mode='lines+markers'))
    fig16.add_traces(go.Scatter(x=d['name'],y=d['position'],name=i,mode='lines+markers',hovertext=d.points))
fig15.show()
In [31]:
fig16['layout']['yaxis']['autorange'] = "reversed"
fig16.show()
In [32]:
data_2019 = races_data.where(races_data['year']==2019).dropna()
data_2019 = data_2019.merge(diverstanding_data,on='raceId').dropna()
data_2019 = data_2019.merge(drivers_data,on='driverId').dropna()
data_2019.drop(columns=['date','time','url_x','url_y','driverStandingsId','driverId','dob','number','nationality','raceId'],inplace=True)
data_2019 = data_2019.sort_values(['round','position'])
data_2019
drivers = data_2019['code'].unique()
In [33]:
fig17 = go.Figure()
fig18 = go.Figure()
for i in drivers:
    d = data_2019.where(data_2019['code'] == i).dropna().sort_values(by='round') 
    fig17.add_traces(go.Scatter(x=d['name'],y=d['points'],name=i,mode='lines+markers'))
    fig18.add_traces(go.Scatter(x=d['name'],y=d['position'],name=i,mode='lines+markers',hovertext=d.points))
fig17.show()
In [34]:
fig18['layout']['yaxis']['autorange'] = "reversed"
fig18.show()
In [35]:
data_2020 = races_data.where(races_data['year']==2020).dropna()
data_2020 = data_2020.merge(diverstanding_data,on='raceId').dropna()
data_2020 = data_2020.merge(drivers_data,on='driverId').dropna()
data_2020.drop(columns=['date','time','url_x','url_y','driverStandingsId','driverId','dob','number','nationality','raceId'],inplace=True)
data_2020 = data_2020.sort_values(['round','position'])
data_2020
drivers = data_2020['code'].unique()
In [36]:
fig19 = go.Figure()
fig20 = go.Figure()
for i in drivers:
    d = data_2020.where(data_2020['code'] == i).dropna().sort_values(by='round') 
    fig19.add_traces(go.Scatter(x=d['name'],y=d['points'],name=i,mode='lines+markers'))
    fig20.add_traces(go.Scatter(x=d['name'],y=d['position'],name=i,mode='lines+markers',hovertext=d.points))
fig19.show()
In [37]:
fig20['layout']['yaxis']['autorange'] = "reversed"
fig20.show()